AI document intake for invoices, forms and email attachments

Where documents come from

The first design question is how many entry points you really have, because each one needs to be watched and normalised. Common sources are a shared mailbox, a client upload page, a shared folder, and files forwarded by staff. Fewer is better: a single intake address or folder that everything is redirected to beats a workflow that has to monitor six places.

For each source, note who sends it (known or unknown senders), what formats arrive (PDF, image, spreadsheet, Word), and whether one email can contain several documents. Multi-document emails and scanned photos taken on phones are the usual sources of trouble, so identify them early.

Classification and extraction

Classification decides what each document is — invoice, quote, contract, ID copy, unknown. Extraction then pulls the fields that matter for that type. Keep the field list short and tied to a decision: if nobody uses a field downstream, do not extract it.

• Define each document type and the exact fields needed, with the format you want (dates, currency, reference numbers).
• Treat 'unknown type' as a first-class outcome that goes to a person, not a forced guess.
• Store the original file alongside the extracted values so anyone can check the source.
• Record which fields the model was unsure about, so the reviewer looks there first.

The human review queue

Model output is a draft. A review queue shows the document next to the extracted fields, highlights uncertain or mismatching values, and lets a person correct and approve in one screen. Nothing is written to the destination system until it is approved.

Decide review rules by consequence, not by feeling. Documents that create payments, change customer records or carry obligations should always be reviewed. Low-risk, well-understood documents may later be allowed through when validation passes — but that is a decision made deliberately after you have watched real results, not a default. We do not quote accuracy percentages, because they depend entirely on your documents; you measure them on your own samples during the build.

Validation against existing records

Many errors are catchable without any model at all. After extraction, check the values against what you already hold, and route mismatches to review.

• Does the supplier or customer exist in your records, and does the name or identifier match?
• Does the reference number match an open order, purchase order or job?
• Is the total consistent with the line items, and is the currency what you expect?
• Is this a duplicate of a document already processed?
• Are required fields present, and are dates plausible?

Filing to your systems

Once approved, the record is written to the destination: a CRM record, a folder with a consistent naming convention, a spreadsheet or database, or an internal tool. We confirm during scoping how each destination can be written to — through an API, an import, or a controlled file drop — rather than assuming a ready-made connector. Filing should be idempotent, so a retry never creates a second copy, and it should log what was written and by whom it was approved.

Exceptions

Real intake is mostly exceptions at the edges: unreadable scans, password-protected files, a document in an unexpected language, an email with no attachment, or a file that is really three documents merged. Give each a defined route — a queue with a named owner and a reason code — so nothing is dropped and nobody has to guess why something is stuck. Review the exception list regularly; recurring exceptions tell you what to change in the rules.

Data handling and access

Documents often hold personal and financial data, so decide before the build where they are processed, who can see them, how long originals and extracted data are kept, and which model provider handles the content. Check the current data terms of any provider involved rather than assuming them, and restrict the review queue to the people who need it. If regulation applies to your documents, your own compliance adviser should confirm the setup; we do not provide legal advice and make no certification claims.

How LATYNEX scopes this

One document type flowing into up to three systems fits the fixed-scope Automation Sprint (€1,690). Several document types, complex validation or high volume is custom scope. Try the Automation Opportunity Finder to check where manual document handling ranks among your processes. The parent service is AI operations automation.